DLTK: State of the Art Reference Implementations for Deep Learning on Medical Images

Abstract

We present DLTK, a toolkit providing baseline implementations for efficient experimentation with deep learning methods on biomedical images. It builds on top of TensorFlow and its high modularity and easy-to-use examples allow for a low-threshold access to state-of-the-art implementations for typical medical imaging problems. A comparison of DLTK's reference implementations of popular network architectures for image segmentation demonstrates new top performance on the publicly available challenge data "Multi-Atlas Labeling Beyond the Cranial Vault". The average test Dice similarity coefficient of $81.5$ exceeds the previously best performing CNN ($75.7$) and the accuracy of the challenge winning method ($79.0$).

Submitted to Medical Imaging Meets NIPS 2017, Code at https://github.com/DLTK/DLTK

Cited by in corpus (4)

• Interactive segmentation of medical images through fully convolutional neural networks
• DoDNet: Learning to segment multi-organ and tumors from multiple partially labeled datasets
• Ivy: Templated Deep Learning for Inter-Framework Portability
• Semantic-Aware Contrastive Learning for Multi-object Medical Image Segmentation